Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States.
Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States.
Front Immunol. 2022 Jun 27;13:889296. doi: 10.3389/fimmu.2022.889296. eCollection 2022.
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual's susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.
全基因组关联研究（GWAS）已经确定了数百种与自身免疫性疾病相关的遗传变异体，为其提供了独特的发病机制见解并指导了新的治疗方法。这些单一的遗传变异体本身通常只会导致疾病风险的微小影响，预测能力有限；然而，当它们聚合在一起（例如，多基因风险评分方法）时，它们可以为多种疾病提供有意义的风险预测。在这篇综述中，我们描述了自身免疫性疾病 GWAS 的最新进展，以及将这方面的知识应用于预测个体易感性/严重程度的实际应用，例如系统性红斑狼疮（SLE）的多基因风险评分方法。我们概述了衍生不同多基因风险评分的方法，并讨论了整合相关特征和不同祖源信息的策略。我们进一步主张需要将临床特征（例如抗核抗体状态）与基因谱分析相结合，以便在临床体征或症状出现之前，更好地识别疾病易感性/严重程度高的患者。最后，我们讨论了在临床护理中应用多基因风险评分方法的未来挑战和机遇。
